Search Results - "Portisch, Jan"

The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings by Portisch, Jan, Paulheim, Heiko

“…Knowledge graph embedding is a representation learning technique that projects entities and relations in a knowledge graph to continuous vector spaces…”
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Walk this Way! Entity Walks and Property Walks for RDF2vec by Portisch, Jan, Paulheim, Heiko

“…RDF2vec is a knowledge graph embedding mechanism which first extracts sequences from knowledge graphs by performing random walks, then feeds those into the…”
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Putting RDF2vec in Order by Portisch, Jan, Paulheim, Heiko

“…The RDF2vec method for creating node embeddings on knowledge graphs is based on word2vec, which, in turn, is agnostic towards the position of context words. In…”
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KERMIT -- A Transformer-Based Approach for Knowledge Graph Matching by Hertling, Sven, Portisch, Jan, Paulheim, Heiko

“…One of the strongest signals for automated matching of knowledge graphs and ontologies are textual concept descriptions. With the rise of transformer-based…”
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Matching with Transformers in MELT by Hertling, Sven, Portisch, Jan, Paulheim, Heiko

“…One of the strongest signals for automated matching of ontologies and knowledge graphs are the textual descriptions of the concepts. The methods that are…”
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Background Knowledge in Schema Matching: Strategy vs. Data by Portisch, Jan, Hladik, Michael, Paulheim, Heiko

“…The use of external background knowledge can be beneficial for the task of matching schemas or ontologies automatically. In this paper, we exploit six…”
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FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs by Portisch, Jan, Hladik, Michael, Paulheim, Heiko

“…This paper presents the FinMatcher system and its results for the FinSim 2021 shared task which is co-located with the Workshop on Financial Technology on the…”
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Entity Type Prediction Leveraging Graph Walks and Entity Descriptions by Biswas, Russa, Portisch, Jan, Paulheim, Heiko, Sack, Harald, Alam, Mehwish

“…The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity…”
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Supervised Ontology and Instance Matching with MELT by Hertling, Sven, Portisch, Jan, Paulheim, Heiko

“…In this paper, we present MELT-ML, a machine learning extension to the Matching and EvaLuation Toolkit (MELT) which facilitates the application of supervised…”
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RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings by Portisch, Jan, Hladik, Michael, Paulheim, Heiko

“…Knowledge graph embedding approaches represent nodes and edges of graphs as mathematical vectors. Current approaches focus on embedding complete knowledge…”
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Visual Analysis of Ontology Matching Results with the MELT Dashboard by Portisch, Jan, Hertling, Sven, Paulheim, Heiko

“…In this demo, we introduce MELT Dashboard, an interactive Web user interface for ontology alignment evaluation which is created with the existing Matching…”
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KGvec2go -- Knowledge Graph Embeddings as a Service by Portisch, Jan, Hladik, Michael, Paulheim, Heiko

“…In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we…”
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Ontology Matching Through Absolute Orientation of Embedding Spaces by Portisch, Jan, Costa, Guilherme, Stefani, Karolin, Kreplin, Katharina, Hladik, Michael, Paulheim, Heiko

“…Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which…”
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Challenges of Linking Organizational Information in Open Government Data to Knowledge Graphs by Portisch, Jan, Fallatah, Omaima, Neumaier, Sebastian, Jaradeh, Mohamad Yaser, Polleres, Axel

“…Open Government Data (OGD) is being published by various public administration organizations around the globe. Within the metadata of OGD data catalogs, the…”
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Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019 by Abbas, Nacira, Alghamdi, Kholoud, Alinam, Mortaza, Alloatti, Francesca, Amaral, Glenda, d'Amato, Claudia, Asprino, Luigi, Beno, Martin, Bensmann, Felix, Biswas, Russa, Cai, Ling, Capshaw, Riley, Carriero, Valentina Anita, Celino, Irene, Dadoun, Amine, De Giorgis, Stefano, Delva, Harm, Domingue, John, Dumontier, Michel, Emonet, Vincent, van Erp, Marieke, Arias, Paola Espinoza, Fallatah, Omaima, Ferrada, Sebastián, Ocaña, Marc Gallofré, Georgiou, Michalis, Gesese, Genet Asefa, Gillis-Webber, Frances, Giovannetti, Francesca, Buey, Marìa Granados, Harrando, Ismail, Heibi, Ivan, Horta, Vitor, Huber, Laurine, Igne, Federico, Jaradeh, Mohamad Yaser, Keshan, Neha, Koleva, Aneta, Koteich, Bilal, Kurniawan, Kabul, Liu, Mengya, Ma, Chuangtao, Maas, Lientje, Mansfield, Martin, Mariani, Fabio, Marzi, Eleonora, Mesbah, Sepideh, Mistry, Maheshkumar, Tirado, Alba Catalina Morales, Nguyen, Anna, Nguyen, Viet Bach, Oelen, Allard, Pasqual, Valentina, Paulheim, Heiko, Polleres, Axel, Porena, Margherita, Portisch, Jan, Presutti, Valentina, Pustu-Iren, Kader, Mendez, Ariam Rivas, Roshankish, Soheil, Rudolph, Sebastian, Sack, Harald, Sakor, Ahmad, Salas, Jaime, Schleider, Thomas, Shi, Meilin, Spinaci, Gianmarco, Sun, Chang, Tietz, Tabea, Dhouib, Molka Tounsi, Umbrico, Alessandro, Berg, Wouter van den, Xu, Weiqin

“…One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and…”
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